Terminologies, ontologies, and vocabularies are valuable tools for conceptualizing complex relationships in certain application fields such as medical terminologies and organizational charts of large enterprises. These knowledge bases consist of a collection of concepts describing entities in the application field, attributes describing them, and relationships directed between concepts. Concepts are connected by hierarchical (“IS-A”) relationships, wherein an IS-A relationship from one concept to another concept indicates that the former is more specific than the latter. The power of terminologies comes out of their visual display, which is much easier to mentally process than a textual representation. However, for large terminologies—practically all useful terminologies are large—the visual display also becomes difficult to comprehend. To deal with this problem, it is possible to derive abstraction networks from large terminologies, which maintain the gestalt of a larger terminology. Such a network should be displayable as a compact diagram in its own right.
In previous work, the present inventors have carried out structural analyses of hierarchies yielding two types of high-level abstraction networks: area taxonomy and partial-area taxonomy. See, Y. Wang, et al, Structural methodologies for auditing SNOMED, Journal of Biomedical Informatics 40 (5) (2007) 561-581, incorporated herein by reference in its entirety. Each serves to capture the relationship distribution within a hierarchy from a high-level perspective. Both networks are derived based on the respective relationships exhibited by the concepts in the hierarchy. The latter network refines the former by including additional hierarchical grouping knowledge.
However, the foregoing are not adequate to partition and generate a diagram that correctly separates partial areas and overlapping sets from each other to allow the generation of a user-friendly diagram without overlaps.